Data Science Falls Into Many Roles

Data science continues to grow in significance in industry, particularly in industries like software, IT consulting and finance. Last year I shared results from O’Reilly Media’s annual salary survey in this field in Revealing Data Science’s Job Potential. They have just recently released results for their third annual Data Science Salary survey and here are some of their findings.

Over 600 people completed the survey when the questions were opened to anyone, of which the majority (67%) was from the U.S. The data allows a closer look by U.S. regions, particularly California and the Northeast. Additionally 25% were from the Software industry, followed by 10% each for Consulting and for Finance—you can see the salary range breakdown by the industry in Fig 1.

Fig 1. Data Scientist Salary Range by Industry

Per their report, “Despite the fact that this is a ‘data science’ survey, only one-quarter of the respondents have job titles that explicitly identify them as ‘data scientists.’” Some roles like
Team Lead, Manager and Upper Management mask this aspect, but in general the actual role of people who work on analytics is widely spread across job titles. Even with similar jobs the salary range reflects a difference by the title one has (se Fig 2).

Roughly said per these results, the data scientists most commonly work 40 -45 hours a week, are 26 – 35 years old, average over $91,000 a year, largely male, spend 1-4 hours a week in meetings, use primarily Windows or Linux, and have skills in SQL, Excel, Python or R development languages and platforms.

Fig 2. Data Scientist Salary Range by Job Title

What is quite interesting is the break down of tasks that they spend their time on per: extract-transform-load operations, data cleaning, basic exploratory analysis, and machine learning and statistics. The fine details are in their report, but most spend 1-4 hours a week on data cleaning and also on exploratory analysis.

I was surprised that this third year of the survey there were no longitudinal information of change over the past two years. There are new and more in depth questions this time particularly in the skills, but I would have assumed some general trends on salary.

How hard is it to find work in data science? About 35% say it is easy, and 29% say it is an average difficulty, although per their salary chart these map quite closely to their average pay range (see Fig 3). Those who say it is very difficult have a median pay around $60,000, while those who say it is very easy make a median closer to $120,000. No surprise—the higher you go in pay, the easier they say it is to find a job.

Fig 3. The Ease of Finding Data Science jobs

In corollary, this past April, I had met up with Meredith Amdur of Wanted Analytics from Quebec City, Canada at HR Tech London to talk about deeper analytics in finding people with such particular skills. Wanted Analytics business takes data from multiple job search sites and can analyze job types by geographies, skills, and pay levels as currently advertised in the market. I expect such more real time data analysis of job postings gives an alternative point-in-time view of how people are hiring. I expect I will find more job search companies presenting such data analytics at the next HR Tech World Congress at the end of this October.

Rawn Shah is an independent analyst in work culture, management evolution and the future of work. He is also a partner with Ethos VO Ltd (UK). He can be reached on Twitter or LinkedIn.